Deep learning models compute a large number of feature maps from an input image. In practice, one can compute the spectral signature on any/all feature map(s). However, we found that using deepest feature maps leads to better results for the purpose of OOD detection. This is in agreement with the known fact that deeper layers provide more disentangled manifolds [1]}, [2]}. In this study, we only worked with the very last (i.e., deepest) feature maps. In our network, the number of channels in this feature map was 14, which was the length of the spectral signatures in this work.
